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SPIE Defense,Security &amp;Sensing2010

1.
Autonomous energy harvesting embedded sensors for border security
applications
Abhiman Hande*a, Pradeep Shaha, James N. Falascob, Doug Weinerb
a
Texas Micropower Inc, 7920 Belt Line Rd, Suite 1005, Dallas, TX, USA 75254;
b
Crane Wireless Monitoring Solutions, 3301 Essex Drive, Richardson, TX USA 75082
ABSTRACT
Wireless networks of seismic sensors have proven to be a valuable tool for providing security forces with intrusion alerts
even in densely forested areas. The cost of replenishing the power source is one of the primary obstacles preventing the
widespread use of wireless sensors for passive barrier protection. This paper focuses on making use of energy from
multiple sources to power these sensors. A system comprising of Texas Micropower’s (TMP’s) energy harvesting device
and Crane Wireless Monitoring Solutions’ sensor nodes is described. The energy harvesters are suitable for integration
and for low cost, high volume production. The harvesters are used for powering sensors in Crane’s wireless hub and
spoke type sensor network. TMP’s energy harvesting methodology is based on adaptive power management circuits that
allow harvesting from multiple sources making them suitable for underground sensing/monitoring applications. The
combined self-powered energy harvesting solutions are expected to be suitable for broad range of defense and industry
applications. Preliminary results have indicated good feasibility to use a single power management solution that allows
multi-source energy harvesting making such systems practical in remote sensing applications.
Keywords: Energy harvesting, vibrations, piezoelectric, multi-source, power management, wireless sensor networks,
seismic sensors, border security.
1. INTRODUCTION
Border incursion is a problem that is growing in scope and sophistication. Resources are limited and must be optimized,
leveraging today’s most advanced technology, to protect a nation’s borders. Any law enforcement or homeland security
related agency (federal, state and local) tasked with border surveillance is faced with the growing reality that individuals
and groups seeking illegal entry are becoming increasingly sophisticated in their methods of operation. Multiple tunnels
have even been created along the US Mexico border to enable crossing the border unnoticed. Tasked with securing our
nation’s vast borders, the Department of Homeland Security’s Customs and Border Protection (CBP) is responsible for
operating 327 official ports of entry and protecting more than 4,000 miles of border with Canada and 1,900 miles of
border with Mexico.
In order to offset the human and physical aspects of border surveillance and security, Unattended Ground Sensors (UGS)
have helped provide 24-7 autonomous surveillance, over parts of the border area. UGS typically utilize seismic, infrared
and magnetic modalities, either singularly or in combination. Seismic sensors detect ground movement (footsteps, tire
roll, track slap, etc.); infrared sensors respond to the breakage of spatial planes and magnetic sensors recognize metal in
passing vehicles or on a person. To be an effective solution for border security, UGS must be physically small and
unobtrusive, support a long battery life, provide reliable detection and tracking with a very low false alarm rate and be
easy to deploy, integrate, maintain and scale. Ultimately, a highly effective UGS border security solution must be cost-
effective with low required maintenance, providing real-time actionable intelligence.
Sensors have been used for years to detect motion as a border security solution, but this application is evolving to utilize
new technologies that provide a more comprehensive picture of the border, such as Unmanned Aerial Vehicles (UAVs)
along with enhanced ground surveillance technology. Newer, advanced UGS systems, such as the Crane WMS
MicroObserver® Unattended Ground Sensor system, provide classification, tracking and velocity information on targets
of interest while rejecting animals that cause nuisance alarms, all extracted from a single sensing modality with long,
multi-year battery lifetimes.
*
ahande@texasmicropower.com; phone 1 972 804-3502; fax 1 972 985-1290; texasmicropower.com

2.
Border security, however, requires striking a balance between personnel and force multiplication tools such as
infrastructure, technology tools and air assets. Utilizing wirelessly networked UGS systems for detection and
classification, in conjunction with UAVs for mobile surveillance and relay communications, allows the sensors to be
more valuable in the interdiction and apprehension of illegal entries into the United States and to be operated with fewer
personnel. Through the effective utilization of advanced wireless networked UGS technology and energy harvesting
techniques, larger geographic areas can be protected at reasonable costs with almost no maintenance for years, providing
covert awareness of cross-border activity at the lowest total cost of ownership.
While structured deployment of wireless sensor networks with careful placement of nodes and pre-configured topologies
is a possibility in some applications, larger scale border protection applications are better suited to ad-hoc deployments,
as much of the deployment takes place in remote or hostile environments and pre-configuration is costly and not
practical. It would therefore seem that a totally Ad-Hoc networking architecture, using an energy harvesting sensor, is a
natural choice for border protection, with no-priori infrastructure requirements, self-organizing capability, quick
deployment capability, highly scalable nature and low maintenance requirements.
However, an UGS network for border protection is more demanding than other sensor networks, such as home
automation where latency is not critical and the information rate within the overall system is extremely low. The border
protection application is data-centric versus control-centric, which is common in most industrial, machine or habitat
monitoring networks. One of the most important tasks in a border sensor network is information extraction. Due to finite
energy resources, this data gathering process must be energy-efficient in order to extend the lifetime of the network. A
fundamental issue in these tactical wireless sensor networks is the coupling of the distributed nature of parameter
measurement with the need for timely data fusion combined with finite energy resources, which greatly impacts practical
Ad-Hoc topologies.
In this paper, we focus on describing feasibility of energy harvesting (EH) for powering remote sensors in an UGS
network from multiple sources to increase remote sensor lifetime. Multi-source EH is important because any one source
might not have adequate energy density to cater to the power requirements of the sensors. While significant progress has
been made in improving EH transducer efficiency, limited research has been done with respect to improving efficiency
and the power consumption of the power management circuits utilized in the energy conversion process significantly
reducing the impact on overall available energy. The result is either an impractical harvester design and/or overly sized
transducers to compensate for the power management circuit inefficiencies primarily faced by the large power
consumption in existing circuits and discrete implementations. Consequently, system form factors are large and costs are
high, making energy harvesters in several applications impractical. Also, most current solutions focus on EH from a
single source with power management focused on maximizing energy harvested from that specific source.
This paper addresses the above problem by using an EH system that is capable of harvesting energy from multiple
sources with intelligent power management. This coupling provides the potential for maximum power harvesting and
efficient energy delivery under naturally occurring continuously varying environmental conditions that affect the
harvesting transducers and the time varying load demands. Multi-source EH is extremely important to prolong sensor
life especially if applications are interrupt-driven and require critical sensor data to be relayed from time to time. We
evaluate such EH devices for UGS for border security applications and determine feasibility.
2. ENERGY HARVESTING WIRELESS SENSOR NETWORK FRAMEWORK
In order for energy harvesting techniques to be fully utilized we must first look at different network topologies to
determine which most effectively manage energy while maintaining demanding performance capabilities required in a
border security application. Mesh network nodes generally allow transmission only to one’s nearest neighbors as shown
in Figure 1a. Normally, there are multiple routing paths between nodes, making this topology robust to failure of
individual nodes. An advantage of mesh networks is that, although all nodes may be identical and have the same
computing and transmission capabilities, certain nodes can be designated as aggregation nodes performing additional
functions such as data fusion. If an aggregation node fails, another node may take over these functions. The propagation
of sensor data through the mesh allows a sensor network to be extended, in theory to an unlimited range. However, this
limits powering down radios on nodes, as it renders such nodes unavailable for multi-hop communications. The dynamic
management of complex routing information, including information about gateways to external networks, is arguably the
biggest challenge for mesh networks. Mesh networks are not extremely practical where power or latency is a critical

3.
issue, but are useful when the ability to expand the distance of the network is extremely important.
Figure 1. Network topologies.
The star topology (Figure 1b) delivers the lowest overall power consumption but is limited by the transmission distance
of the radio in each sensor endpoint. This typically limits the range of the sensor field to about a 100-meter radius. A
disadvantage is that no alternate communication routes between the endpoints exist; should a path become obstructed,
information from the associated sensor would be lost.
A hub and spoke topology (Figure 1c) is the superior choice for a sensor network that will implement data fusion while
taking advantage of the significant energy gain due to data aggregation. Unlike a mesh network, there is an orderly and
predictable flow of data through the network. Data is naturally aggregated at the hubs, which serve as a convenient point
for data fusion. Data fusion has the effect of limiting the data flow through the mesh section of the network, requiring
lower bandwidth, fewer transmissions and decreased probability of detection by hostile forces and the potential for lower
power usage. Unlike the star topology, a reasonable level of range extension can be realized without a significant
reduction in the overall transmitted bandwidth and without a considerable increase in total energy usage.
3. SENSOR POWER USAGE
Even with a suitable energy efficient topology chosen, close attention must be paid to the sensor design to minimize
energy usage. An energy harvesting powered sensor consists of five elements (Figure 2):
• Environmental Transducer – detection of environmental parameters
• Energy Harvesting Transducer – conversion of ambient energy
• Power Module – collect, store and deliver energy
• MCU – signal analysis
• Radio Link – exfiltration of data
Figure 2. Sensor components.

4.
With a well managed network topology, the radio link’s energy usage is tightly optimized for power usage and is not
typically the main contributor to power consumption. Due to the detection range and latency requirements placed on
detection of intrusions, the environmental transducer and its associated signal analysis in the MCU constitute the main
power draw. Sensors in today’s most advanced Unattended Ground Sensor systems for personnel and vehicle detection,
even those designed for optimized power usage have an average power draw sufficient to require an energy harvester
that is not covert. To practically enable energy harvesting even to supplement batteries, sensor power budgets will need
to be on the order of 5 to 15 mW or less average power, without sacrificing performance. Ongoing work shows this is
achievable in the very near future. Unique sensing techniques and algorithms are taking advantage of the latest
improvements in low power MCU architectures. Through more tightly coupled timing of processing requirements to the
hardware, the MCU can be utilized in its lowest power mode during a majority of the sensing time, thus eliminating the
need for transducer duty cycling or “sleeping” the sensor inputs, which is unacceptable in a border protection scenario.
4. ENERGY HARVESTING FOR SENSOR NODES
The issue of powering the sensors in a UGS network becomes critical when one considers the prohibitive cost of wiring
power to them or replacing their batteries. Obviously, such devices have to be small in size so that they can be
conveniently placed in remote locations and enable covert emplacement. This places a severe restriction on their life if
alkaline or similar batteries are used to power them. To make matters worse, battery technology has not sufficiently
improved in terms of energy density and size over the last decade, especially for low power mobile applications such as
sensor networks. While an effort is being made to improve the energy density of batteries, additional energy resources
need to be investigated to increase the life of these devices.
There are several sources of energy that can be used to power remote sensors. Table 1 compares the power generation
potential of some of the typical EH modalities which include ambient radiation1, temperature gradients2, light3 and
vibrations4,5,6. Among these sources of energy, solar EH through photo-voltaic conversion and vibration energy through
piezoelectric elements provide relatively higher power densities. The energy harvested from any one source is of the
order of a few hundred microwatts using a practical transducer. Therefore, technologists seek to combine multiple
sources in order to boost the harvesting capability. However, this requires efficient power management (PM) circuit
design and possibly a single PM solution to minimize size and cost. Consequently, this paper explores the feasibility of
using piezoelectric vibration and solar EH for powering UGS network sensors for border security applications.
Table 1. Power densities of energy harvesting technologies.
Energy Scavenging Power Density Information Source
Source (µW/cm3)
Solar (Outdoors) 15,000 – Direct Sun Commonly Available
150 – Cloudy Day
Solar (Indoors) 6 – Office Desk Experiments
Vibrations 100 - 200 Roundy et. al.
Acoustic Noise 0.003 @ 75 dB Theory
0.96 @ 100 dB
Daily Temp. Variation 10 Theory
Temp. Gradient 15 @ 10o Celsius Stordeur & Stark 1997
Piezo Shoe Inserts 330 Starner 1996
There are three key components for successful development and commercialization of a practical EH sensor. The first
component involves design of high efficiency transducers. The second involves design of high efficiency power
management, and the third component is the energy storage device that must have low leakage, cost, and form factor and
high cycle life. Texas Micropower, Inc. (TMP) is developing thin-film MEMS piezoelectric cantilevers based on a new
composition that has much higher energy density as compared to contemporary compositions. The design for integrating
multiple EH transducers involves complex tradeoffs due to the interaction of several factors such as the characteristics of
the energy sources, power supply requirements and power management features of the embedded system, and
application behavior. It is, therefore, essential to thoroughly understand and judiciously exploit these factors in order to
maximize energy efficiency of the harvesting modules. However, as will be explained later, there is a method to design
smart power management circuits that can adjust their operation to maximize power harvesting and consequently,
system efficiency.

5.
The key technical challenges of such a system requires development of a single power management solution that can
adapt to different transducer characteristics for maximum energy transfer (e.g. different piezoelectric transducers will
have different source capacitance, and therefore, the power management must tune itself to match the source
impedance). The multi-source EH solution will require integration of power management circuits designed for EH from
each natural source (e.g. integration of solar maximum power tracking circuits with piezoelectric impedance matching
circuits). This power module must be capable of adapting to the characteristics of the EH source characteristics (e.g.
light intensity, frequency of vibration, etc.).
4.1 Energy harvesting from vibrations
Mechanical energy can be converted to electrical energy using piezoelectric, electromagnetic, or electrostatic
mechanisms. However, the mechanical-to-electrical energy conversion using the piezoelectric effect provides a smaller,
lighter, and more efficient method to harvest vibration energy. Piezoelectric converters have been shown to possess three
times higher energy density as compared to the other converter types7. The generated power is proportional to the square
of the voltage, (P ∝ V2), thus further improvements in the energy density can be obtained by enhancing the output
voltage (Voc) magnitude.
The primary factors for choosing a piezoelectric material for EH are the piezoelectric strain constant (d) and the
piezoelectric voltage constant (g). At sufficiently low frequencies, a piezoelectric sensor can be modeled as a parallel
plate capacitor. The electric energy available under ac stress excitation from a parallel plate capacitor is:
1
U = CV 2 (1)
2
or
u = 1 2 (d .g ) ⋅ ( X )
2
(2)
where: U = energy, C = capacitance, V = voltage, X = stress, and u = energy density8.
This equation illustrates the importance of d and g constants in fabricating piezoelectric harvesting devices. A
piezoelectric material with high energy density is characterized by a large product of piezoelectric voltage constant (g)
and piezoelectric stress constant (d), given as (d.g), and a high magnitude of g coefficient. The ideal material should
therefore combine the highest possible product of the (d.g) and the highest possible g coefficient for EH applications.
TMP has exclusive license to a composition that has the highest reported (d.g product) as shown in Figure 3 that
compares the composition to all the other commercially available PZT-based sensor/actuator ceramic materials. The
composition, developed at the University of Texas at Arlington (UTA), combines the giant magnitude of the
piezoelectric voltage coefficient that is comparable to that of piezoelectric polymer, and a giant magnitude of (d.g)
product that is comparable to that of relaxor-based single crystals. In the end, this composition has the highest figure of
merit compared to many commercial PZT compositions. This material is currently being developed in thin-film form
that is suitable for EH device and system integration9.
18000
TMP/UTA
Morgan
DongIl
15000
Ferroperm
APC
12000
APC
Fuji
EDO
Channel
DongIl
Ferroperm
EDO
9000
Morgan
Channel
6000
10 20 30 40 50 60
Voltage Sensitivity g33 (x 10-3 Vm/N or m2/C)
Figure 3. The newly developed composition (UTA) has significantly higher (d.g) product than many commercial compositions.

6.
A typical system for implementing vibration energy harvesting from a piezoelectric element is shown in Figure 4. In
order to obtain maximum power transfer, the input impedance of the converter (Zin) should match that of the source (Zo,
reflected at the output of the rectifier). The transducer impedance (Zo) is given by
1
Z = (3)
0 4 f SourceC
Source
where fSource is the frequency of a piezoelectric cantilever based vibration transducer, and C is the transducer series
capacitance. Therefore, as the frequency decreases, the impedance increases and vice versa. The rectifier output voltage,
(Vrect) across Cs determines the amount of power delivered to the converter, and consequently, the load. The power
delivered to the converter is,
V2 Z
P = rect ≅V 2 O (4)
0 Z
in
Speak
(Z O + Z in )2
From Figure 4 and equation 3, it can be seen that Zin must equal to Zo to obtain maximum Vrect which is approximately
half of the peak transducer voltage (VSpeak).
Switching converter (buck or
buck-boost) or Switched
capacitor converter
Piezoelectric generator
C Rs
DC-DC
Converter
Cs Secondary
Vs battery (Thin
film, lithium,
NiMH, etc.)
Schottky diodes or
Synchronous rectifier
Figure 4. EH circuit for piezoelectric converters.
Figure 5 shows how a switch mode buck-boost converter implements the required impedance matching. Any non-
isolated switch mode DC-DC converter such as a buck, boost, buck-boost, or boost-buck topology may be employed as
the converter. It is, however, important to fix the converter’s operation in the discontinuous conduction mode (DCM) for
maximum power harvesting. DCM mode is preferred over continuous conduction mode (CCM), because the former
avoids the reverse recovery problem of the diode10. Also, for certain topologies such as buck-boost operating in DCM,
the average input impedance Zin does not depend upon the output energy storage device voltage VB1 and this simplifies
controller design. Buck converters, however, require a high input to output voltage differential to operate. The optimal
converter duty cycle depends on output filter inductance L, and the switching frequency fsw.
Figure 5. DC-DC buck-boost converter with battery load.

7.
Z0 is required for designing the DC-DC converter with average input impedance (Zin) equal to Z0 for maximum power
transfer. It can be clearly observed that different transducers will have different capacitances which will result in
different transducer impedances. Similar observations can be made for transducers operating at different source
frequencies. Therefore, it is important that the converter can predict the Z0 so that it automatically adjusts its Zin.
4.2 EH from solar energy
Figure 6 shows the typical EH solution for harvesting solar energy where a DC-DC converter maximizes the energy
transfer.
Switching converter
(buck or buck-boost) or
Switched capacitor
converter
DC-DC
Converter
Secondary battery
Load (Thin film, lithium,
S1 Cs NiMH, etc.) or
Ultracapacitor
Figure 6. EH circuit for solar cells.
Figure 7 shows the V-I characteristics of a single 25-mm x 60-mm silicon photovoltaic (PV) panel under different
lighting conditions. These curves show the relationship between two key parameters, the open circuit voltage (VOC) and
the short circuit current (ISC), that influence energy conversion. Each parameter form the x- and y- intercepts of the V-I
curve, respectively as shown in Figure 7. The curves demonstrate that a solar panel behaves as a voltage limited current
source and that the short circuit current ISC is proportional to the light intensity.
100000
Full Sun (95000 lx)
10000
3300 lx
1000
Current (uA)
300 lx
100
10
1
0 1 2 3 4 5 6
Voltage (V)
Figure 7. Solar panel V-I characteristics
A power management scheme is required to regulate the power from what is essentially a current source transducer, and
deliver it to storage devices such as rechargeable batteries or ultracapacitors. Efficient power management circuits utilize
maximum power point tracking (MPPT) techniques to harvest the most energy possible from a given light intensity. For
example, Figure 7 indicates that the selected panel produces a maximum power of about 200 micro-Watts at two Volts
when the light intensity is about 300 lux. Analog circuits, such as the DC-DC buck-boost converter shown in Figure 8,
track the optimum voltage VMPPT for different light intensities more efficiently as compared to circuits using higher

8.
power consumption microcontrollers11.
The DC-DC buck-boost converter monitors the voltage across the input capacitor until it increases above VMPPT. At this
point the MOSFET switch is turned on to route energy to the storage device. The converter turns off when the input
capacitor voltage decreases below VMPPT thus maintaining the average voltage at VMPPT, which is the optimum point for
maximum power transfer. Since VOC scales linearly with light intensity and VMPPT scales linearly with VOC, then
measuring VOC leads to a simple estimation of VMPPT. The designers must complete this characterization for each solar
cell prior to its implementation in a control circuit. It is, however, possible to use a fixed reference voltage given known
lighting conditions, such as indoor applications.
Q1 Ibat
VPV D1 D2
Irect
Vctrl
To
+ +
Sensor
Vrect Cs L VC2 C2 B1 VB1
PV - -
Vrect
MPP Estimation
Q2
Figure 8. DC-DC buck-boost converter for solar energy harvesting.
4.3 Multi-source energy harvesting with intelligent power management
The core of any EH module is the harvesting circuit, which draws power from the transducer (e.g. solar panels,
piezoelectric bimorphs, temperature differential, etc.), and manages energy storage. Figure 9 shows the simplified
diagram of the proposed EH power management (PM) system architecture. The most important consideration in the
design of this circuit is to maximize energy efficiency, enhance device reliability, and consequently, lengthen the life of
the sensor. It is important to note that no matter what the source energy is, the PM circuit needs to be designed so that its
input impedance matches that of the source. As noted earlier, solar cells and PZT bimorphs have high output impedance.
This results in considerable loading and therefore, minimal output power. If this impedance is matched adaptively, there
will always be maximum power routed and stored in the energy storage device. The PM circuit is responsible for
adaptively calculating the output impedance of the transducer and consequently, adjusting key parameters to maximize
charging of the energy storage device.
Solar/Vibrations/ AC/DC Rechargeable battery,
Thermal, etc. Conversion Ultracapacitor, etc.
Energy
Optional DC/DC
Tranducer Storage
Rectification Converter Device
PWM
Controller
Vrect, Irect Vbat, Ibat
Figure 9. Adaptive multi-source power management architecture.
As noted earlier, it is feasible to use switch mode DC-DC buck, boost and buck-boost converters to maximize harvested
power. The optimal duty cycle of the converter is dependent on output filter inductance (L) and switching frequency (fs).

9.
These converters can significantly reduce the matching impedance of the circuit so that typical loads such as batteries
that have impedance of the order of few hundred ohms can be efficiently charged. However, there has been limited work
towards development of adaptive converters that facilitate accurate impedance matching. Most of the converters used are
meant for providing regulated output voltage to resistive loads with no real concern on loading effects of the source. This
is because these power supplies operate using near ideal voltage sources and simply perform the required energy
conversion. Existing system level solutions that incorporate custom DC-DC converters for impedance matching are
tuned to a specific frequency and transducer for specific application environments. These power management strategies
are not generic and therefore, cannot be used over a wide spectrum of frequencies and transducers. Moreover, some of
these methods use power hungry digital signal processors (DSPs) for implementing PWM control algorithms12. This
strategy results in excessive power consumption in the power management solution and therefore, does not allow
feasibility in practical applications.
Figure 9 shows the active PM architecture. A simple feedforward strategy is used wherein the average input voltage
(Vrect) and current (Irect) of the adaptive PM circuit is measured to determine the average input resistance (Rin) given by:
Vrect/Irect. Alternatively, input frequency can be measured (for vibration transducers) and converted to a voltage
(frequency to voltage converter) to determine input impedance. The converter input impedance can then be adjusted to
this value by varying D to obtain accurate impedance matching. Similarly, fs or a combination of fs and D can be varied
to obtain required Rin. Such a method can easily be implemented on a DSP or FPGA but this is impractical due to lower
power levels (mWs) being harvested as compared to power consumption of the control circuits. The controller uses a
simple formula to determine required D based on the Rin measurement. The calculated D value is passed onto a standard
pulse width modulator (PWM) and MOSFET driver circuit to turn on the MOSFET at the desired D. Although, the
converter output voltage (Vbat) and current (Ibat) is shown to be fedback to the controller, this is not necessary if operation
is restricted in DCM. This reduces overhead and simplifies controller design.
5. PRELIMINARY RESULTS
5.1 Energy harvesting from vibrations and solar cells
In order to harvest energy from vibrations, the first step involves obtaining the vibration spectrum from the source or
structure. It is important to determine the range of frequencies at which maximum force (acceleration) occurs. In this
paper, data has been obtained from ground and bridge vibrations to observe feasibility of designing vibration EH
systems for UGS sensors for border security and perimeter monitoring. The Texas Micropower Inc. TMP-DL-2 vibration
data logger was used to measure the data. This device contains a 3-axis accelerometer and is well-suited to measure
vibration data upto ±6 gpeak with ±0.01gpeak resolution and sampling rate upto 500 Hz or 1000 Hz13.
Figure 10 shows one set of data obtained from bridge deck vibrations during low traffic situations. Acceleration of about
0.02 gpeak was obtained in the 5 - 20 Hz frequency range during relatively low traffic situations on bridges. Similar data
has been obtained for staircase foot vibrations (with resonant frequency of 20 - 30 Hz at acceleration of about 0.05 g)14.
These results give good insight of typical vibration profiles that can be expected at fences and posts.
Figure 10. Frequency spectrum of a bridge deck under low traffic conditions.

10.
Adequate macro-harvesters were designed for these specifications. Initial experiments have been performed by
designing a buck-boost converter for vibration EH using piezoelectric bimorphs. A buck-boost converter was chosen for
the design because under DCM, at fixed values of fs and D, it is possible to have a constant Rin which can be fixed at the
required matching impedance. The prototype was capable of delivering > 150 µW continuous power at low excitation
levels of 0.07 gpeak at efficiencies of the order of 60-70%15,16. With a 5 - 15 mW sensor average power requirement
budget, it can be observed that the size of these bulk piezoelectric harvesters will be fairly large. For example, for 0.02 g
sustained vibrations, we anticipate the harvester size to be about 300 cm3 for a 5 mW average sensor load. Similarly, we
anticipate the harvester size to be about 800 cm3 for a 15 mW average sensor load.
5.2 Multi-source energy harvesting
In order to enhance the net energy harvested, it might be necessary to incorporate multiple energy harvesting sources. In
a practical system, this needs to be implanted with a low power transfer overhead rather than using a custom power
management system for each source. A single power management solution shown in Figure 11 that allows energy
harvesting from multiple sources can provide this flexibility.
Power Management
DC/DC
Vpzt converter
AC/ Vrect
DC Switch Impedance
PZT Matching Circuit
B
Vctrl1 a
VIBRATION SENSE t
t
e
DC/DC B
r
VPV converter a
y
t
MPPT Tracking t
Solar Switch M
e
Algorithm Loop a
r
n
y
Vctrl2 a
LIGHT SENSE
g
e
m
DC/DC e
VTEG Charge converter n
Pump t
TEG Max Power
TEG Vboost
Switch Loop
Vctrl3
TEMPERATURE SENSE
Figure 11. Power management for multi-source energy harvesting.
We have developed a multi-source energy harvesting power management solution for low power sensors and electronic
systems which efficiently enables both solar energy harvesting from photo-voltaic cells & vibration energy harvesting
from piezoelectric transducers. The harvested energy is stored in a rechargeable 3.6 VDC lithium-ion battery. It is
designed with the intent of low power consumption from circuit components and high conversion efficiency delivering
unmatched performance. The figure also includes thermoelectric generators (TEGs), however initial results have focused
on only vibrations and solar. Note that TEGs have a low output voltage, and a relatively large current. This poses a
challenge when charging Li-ion batteries. If the TEG is designed to deliver a higher voltage at the expense of lower
current, the cost increases. Therefore, for most commercially available TEGs, the output voltage is small, and the current
is high. To allow TEGs to be integrated to the power management, the figure shows a charge pump followed by a boost
converter.
The harvester’s vibration module consists of a full-wave rectifier circuit for converting AC to DC and a buck-boost
converter to enable adequate impedance matching and maximum power transfer to the load. The circuit is optimized to
work on typical low frequency vibrations that are obtained on automobiles, rail cars, industrial machines, bridges and
other places which are subject to vibrations. The harvester’s solar module also uses a buck-boost converter that allows
maximum power transfer to load based on a desired constant voltage maximum power point (MPP) value. The circuit is

11.
optimized to work on typical ambient light sources such as sun light, incandescent or fluorescent lights. A battery
protection circuit helps in protecting the battery from over-charging and over-discharging. Figure 12 shows the prototype
system with power management circuit and lithium polymer secondary battery as the energy storage device. The overall
form factor of the complete prototype is estimated to be 40 x 40 x 85 mm3 and is expected to be much smaller once the
electronics are integrated into a single chip.
Power management board Vibration power management
Vibration
Piezoelectric transducer
bimorphs inputs
VDD to
Solar cell sensor
inputs
Amorphous solar cells Solar power management
Figure 12. Solar and vibration multi-source energy harvesting system.
For Solar EH, the MPP voltage VMPP can is set or modified by replacing external resistors based on a simple formula.
Similarly for vibration EH, the matching impedance can be set by replacing an external inductor using on a simple
formula. Initial results have been measured using solar cells under indoor lighting conditions with characteristics shown
in Figure 7 and a bulk Smart Material based piezoelectric bimorph. An intensity of 300 lux was used similar to that
obtained under indoor fluorescent lighting conditions. At this light intensity the power output to the battery from the
solar cells equaled about 160 µW. The vibration intensity (excitations) for the piezoelectric transducer was varied upto
0.1 gpeak and the power output to the battery from each source was quantified. The x axis indicates the corresponding
rectifier voltage (Vrect) at a given excitation (refer Figure 11). As seen from Figure 13, the power management strategy
enhanced energy harvested with an efficiency of > 80%. For example, for a Vrect of 3VDC, the output power from
vibrations equaled about 148 µW. The power management routed the output from both vibrations and solar so that the
total output power equaled about 292 µW at Vrect = 3VDC.
500
combined
450
400
Output power (µW)
350
vibrations
300
250
200 solar (indoors)
150
100
50
0
1.5 2 2.5 3 3.5 4
Vrect (vibration)
Figure 13. Multi-source energy harvesting results.
Similar results were also obtained under outdoor lighting conditions. However, in this case, the solar cell output power
(few mWs) dominates the total power routed to the storage device and therefore, these results are not included.
6. CONCLUSIONS